The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also be inventions.
Various electronic devices today are typically operated by a user interacting with a touch sensitive screen. This feature is particularly a characteristic of the recent generation of smart phones. Typically, the touch sensitive screens respond to finger contact to activate the display for further processes. Contact may also be made using tools such as a stylus or other parts of the hands. The fingers and other contacts made to the touch sensitive screen generally appear as an activated point or blob (i.e., region). However, when touch contacts occur on the edge of the touch screen, only a portion of the touch contact can be digitized.
The touch sensitive screen may be associated with a classification engine which is normally trained on real world touch event data from users. However, because edge contacts are rare in ordinary use, classifiers (in the classification engine) receive few edge training instances. In response, the classification accuracy of edge touch events tends to be lower. Further, because less of the touch contact is visible, there is less data to work with. This leads to several problems, most notably that classification algorithms may over-fit to the limited data. Secondly, edge contacts appear very different to full-contact, ordinary touches, leading to bi-modal (or even multi-modal) distributions of key characteristics to which some classification algorithms are ill-suited to accommodate. There is therefore a need to mitigate the potential problems associated with edge touch events that could otherwise reduce the accuracy of such classification analysis.